【OpenCV学习笔记】二十七、轮廓特征属性及应用(四)——正外接矩形

轮廓特征属性及应用(四)——正外接矩形

1.轮廓正外接矩形——boundingRect（）

2.完成了三个小应用：正外接矩形的查找绘制、分割硬币轮廓、简单车牌字符分割

先上ppt：

代码：

正外接矩形的查找绘制：

///正外接矩形的查找绘制
#include "opencv2/opencv.hpp"
using namespace cv;
#include 
using namespace std;
int main()
{
	//1.查找轮廓
    //1.1查找轮廓前的预处理（灰度图，阈值化）
	Mat srcImg = imread("12.jpg",CV_LOAD_IMAGE_COLOR);
	imshow("srcImg", srcImg);
	Mat copyImg = srcImg.clone();
	cvtColor(srcImg,srcImg,CV_BGR2GRAY);
	threshold(srcImg,srcImg,100,255,CV_THRESH_BINARY);
	imshow("threshold",srcImg);
	vector > contours;
	vector hierarcy;//没用到
	//1.2查找轮廓
    findContours(srcImg,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);//最外层轮廓
	//1.3绘制所有轮廓
	drawContours(copyImg,contours,-1,Scalar(0,255,0),1,8);
	//2.由轮廓确定正外接矩形
	int width = 0;
	int height = 0;
	int x = 0;
	int y = 0;
	//2.1 定义Rect类型的vector容器boundRect存放正外接矩形，初始化大小为contours.size()即轮廓个数
	vector boundRect(contours.size());
	//2.2 遍历每个轮廓
	for (int i = 0; i < contours.size(); i++)
	{
		//2.3由轮廓（点集）确定出正外接矩形
		boundRect[i] = boundingRect(Mat(contours[i]));
		//2.4获得正外接矩形的左上角坐标及宽高
		width = boundRect[i].width;
		height = boundRect[i].height;
		x = boundRect[i].x;
		y = boundRect[i].y;
		//2.5用画矩形方法绘制正外接矩形
		rectangle(copyImg,Rect(x,y,width,height),Scalar(0,0,255),1,8);
	}
	imshow("轮廓和正外接矩形", copyImg);
	waitKey(0);
	return 0;
}

运行结果：

分割硬币轮廓：

///分割硬币轮廓
#include "opencv2/opencv.hpp"
using namespace cv;
#include 
using namespace std;
int main()
{
	//1.查找轮廓
	//1.1查找轮廓前的预处理（灰度图，阈值化）
	Mat srcImg = imread("33.png",CV_LOAD_IMAGE_COLOR);
	imshow("srcImg", srcImg);
	Mat copyImg = srcImg.clone();
	cvtColor(srcImg,srcImg,CV_BGR2GRAY);
	threshold(srcImg,srcImg,100,255,CV_THRESH_BINARY);
	imshow("threshold",srcImg);
	//*1.2增加了膨胀和腐蚀
	//因为有一个轮廓有断点，导致外接矩形是两个小的而不是一个整的，故要膨胀，将断点连起来
	//1.2.1定义kernel
	Mat kernel = getStructuringElement(MORPH_RECT,Size(5,5),Point(-1,-1));
	//1.2.2膨胀
	dilate(srcImg,srcImg,kernel,Point(-1,-1));
	//1.2.3腐蚀
	erode(srcImg,srcImg,kernel,Point(-1,-1));
	imshow("膨胀和腐蚀", srcImg);
	//1.3查找轮廓
	vector > contours;
	vector hierarcy;//没用到
	findContours(srcImg,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);//最外层轮廓
	//1.4绘制所有轮廓
	drawContours(copyImg,contours,-1,Scalar(0,255,0),1,8);
	//2.由轮廓确定正外接矩形
	int width = 0;
	int height = 0;
	int x = 0;
	int y = 0;
	//2.1 定义Rect类型的vector容器boundRect存放正外接矩形，初始化大小为contours.size()即轮廓个数
	vector boundRect(contours.size());
	//2.2 遍历每个轮廓
	for (int i = 0; i < contours.size(); i++)
	{
		//2.3由轮廓（点集）确定出正外接矩形
		boundRect[i] = boundingRect(Mat(contours[i]));
		//2.4获得正外接矩形的左上角坐标及宽高
		width = boundRect[i].width;
		height = boundRect[i].height;
		x = boundRect[i].x;
		y = boundRect[i].y;
		//*2.5 对正外接矩形进行筛选（过滤掉小的）
		if (width>40 && height > 40)
		{
			//2.6用画矩形方法绘制正外接矩形
			rectangle(copyImg, Rect(x, y, width, height), Scalar(0, 0, 255), 1, 8);
		}
	}
	imshow("轮廓和正外接矩形", copyImg);
	waitKey(0);
	return 0;
}

运行结果：

简单车牌字符分割：

///简单车牌字符分割
#include "opencv2/opencv.hpp"
using namespace cv;
#include 
using namespace std;
int main()
{
	//1.查找轮廓
	//1.1查找轮廓前的预处理（灰度图，阈值化）
	Mat srcImg = imread("Car.jpg", CV_LOAD_IMAGE_COLOR);
	imshow("srcImg", srcImg);
	Mat copyImg = srcImg.clone();
	cvtColor(srcImg, srcImg, CV_BGR2GRAY);
	threshold(srcImg, srcImg, 100, 255, CV_THRESH_BINARY);
	imshow("threshold", srcImg);
	//1.2查找轮廓
	vector > contours;
	vector hierarcy;//没用到
	findContours(srcImg, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);//所有轮廓
	//1.3绘制所有轮廓
	drawContours(copyImg, contours, -1, Scalar(0, 255, 0), 1, 8);
	//2.由轮廓确定正外接矩形
	int width = 0;
	int height = 0;
	int x = 0;
	int y = 0;
	//2.1 定义Rect类型的vector容器boundRect存放正外接矩形，初始化大小为contours.size()即轮廓个数
	vector boundRect(contours.size());
	//2.2 遍历每个轮廓
	for (int i = 0; i < contours.size(); i++)
	{
		//2.3由轮廓（点集）确定出正外接矩形
		boundRect[i] = boundingRect(Mat(contours[i]));
		//2.4获得正外接矩形的左上角坐标及宽高
		width = boundRect[i].width;
		height = boundRect[i].height;
		x = boundRect[i].x;
		y = boundRect[i].y;
		//*2.5 对正外接矩形进行筛选（过滤掉过小及过大的）
		if (width>(1.0 / 12)*srcImg.cols && width<(1.0 / 7)*srcImg.cols 
			 &&height >(1.0 / 6)*srcImg.rows&& height < (5.0 / 6)*srcImg.rows)
		{
			//2.6用画矩形方法绘制正外接矩形
			rectangle(copyImg, Rect(x, y, width, height), Scalar(0, 0, 255), 1, 8);
			//*2.7 通过ROI保存分割出的车牌字符
			//2.7.1 定义ROI
			Mat ROI = copyImg(Rect(x,y,width,height));
			//2.7.2 通过sprintf格式化文件存储名name
			char name[20] = {0};
			sprintf(name,"E:\\temp\\%d.jpg",i);
			//2.7.3 写ROI到本地
			imwrite(name,ROI);
		}
	}
	imshow("轮廓和正外接矩形", copyImg);
	waitKey(0);
	return 0;
}

运行结果：